Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China.
Department of Oncology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Nucleic Acids Res. 2023 Jan 6;51(D1):D1160-D1167. doi: 10.1093/nar/gkac677.
Genome-wide association studies (GWASs) underlying case-control design have uncovered hundreds of genetic loci involved in tumorigenesis and provided rich resources for identifying risk factors and biomarkers associated with cancer susceptibility. However, the application of GWAS in determining the genetic architecture of cancer survival remains unestablished. Here, we systematically evaluated genetic effects at the genome-wide level on cancer survival that included overall survival (OS) and cancer-specific survival (CSS), leveraging data deposited in the UK Biobank cohort of a total of 19 628 incident patients across 17 cancer types. Furthermore, we assessed the causal effects of risk factors and circulating biomarkers on cancer prognosis via a Mendelian randomization (MR) analytic framework, which integrated cancer survival GWAS dataset, along with phenome-wide association study (PheWAS) and blood genome-wide gene expression/DNA methylation quantitative trait loci (eQTL/meQTL) datasets. On average, more than 10 traits, 700 genes, and 4,500 CpG sites were prone to cancer prognosis. Finally, we developed a user-friendly online database, SUrvival related cancer Multi-omics database via MEndelian Randomization (SUMMER; http://njmu-edu.cn:3838/SUMMER/), to help users query, browse, and download cancer survival results. In conclusion, SUMMER provides an important resource to assist the research community in understanding the genetic mechanisms of cancer survival.
全基因组关联研究（GWAS）基于病例对照设计，已经发现了数百个与肿瘤发生相关的遗传位点，为识别与癌症易感性相关的风险因素和生物标志物提供了丰富的资源。然而，GWAS 在确定癌症生存的遗传结构中的应用尚未确定。在这里，我们利用 UK Biobank 队列中 17 种癌症类型的 19628 名患者的数据集，系统地评估了全基因组水平上对癌症生存（包括总生存（OS）和癌症特异性生存（CSS））有影响的遗传效应。此外，我们通过孟德尔随机化（MR）分析框架评估了风险因素和循环生物标志物对癌症预后的因果影响，该框架整合了癌症生存 GWAS 数据集，以及表型全基因组关联研究（PheWAS）和血液全基因组基因表达/DNA 甲基化定量性状基因座（eQTL/meQTL）数据集。平均而言，超过 10 个特征、700 个基因和 4500 个 CpG 位点与癌症预后有关。最后，我们开发了一个用户友好的在线数据库，即通过孟德尔随机化的癌症多组学生存数据库（SUrvival related cancer Multi-omics database via MEndelian Randomization，SUMMER；http://njmu-edu.cn:3838/SUMMER/），帮助用户查询、浏览和下载癌症生存结果。总之，SUMMER 提供了一个重要的资源，以帮助研究社区了解癌症生存的遗传机制。
